Created by: ghm

硬件信息: GPU:v100 显存:16GB CPU:8 RAM:32GB 环境配置: Python版本:python 3.5 框架版本:PaddlePaddle 1.5.1 代码: from paddle.fluid.framework import switch_main_program import paddlehub as hub import paddle.fluid as fluid

module = hub.Module(name="ernie",version='1.0.2') dataset = hub.dataset.ChnSentiCorp()

reader = hub.reader.ClassifyReader( dataset=dataset, vocab_path=module.get_vocab_path(), max_seq_len=128)

strategy = hub.AdamWeightDecayStrategy( weight_decay=0.01, warmup_proportion=0.1, learning_rate=5e-5, lr_scheduler="linear_decay", optimizer_name="adam")

config = hub.RunConfig( use_cuda=True, num_epoch=1, checkpoint_dir="ernie_txt_cls_turtorial_demo", batch_size=32, log_interval=10, eval_interval=50, strategy=strategy)

inputs, outputs, program = module.context( trainable=True, max_seq_len=128)

Use "pooled_output" for classification tasks on an entire sentence.

pooled_output = outputs["pooled_output"]

feed_list = [ inputs["input_ids"].name, inputs["position_ids"].name, inputs["segment_ids"].name, inputs["input_mask"].name, ]

cls_task = hub.TextClassifierTask( data_reader=reader, feature=pooled_output, feed_list=feed_list, num_classes=dataset.num_labels, config=config)

cls_task.finetune_and_eval() #这句话执行出错 错误信息: ---------------------------------------------------------------------------EnforceNotMet Traceback (most recent call last) in ----> 1 cls_task.finetune_and_eval() /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages/paddlehub/finetune/task.py in finetune_and_eval(self) 504 505 def finetune_and_eval(self): --> 506 return self.finetune(do_eval=True) 507 508 def finetune(self, do_eval=False): /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages/paddlehub/finetune/task.py in finetune(self, do_eval) 509 # Start to finetune 510 with self.phase_guard(phase="train"): --> 511 self.init_if_necessary() 512 self._finetune_start_event() 513 run_states = [] /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages/paddlehub/finetune/task.py in init_if_necessary(self) 166 if not self.is_checkpoint_loaded: 167 self.is_checkpoint_loaded = True --> 168 if not self.load_checkpoint(): 169 self.exe.run(self._base_startup_program) 170 /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages/paddlehub/finetune/task.py in load_checkpoint(self) 487 self.config.checkpoint_dir, 488 self.exe, --> 489 main_program=self.main_program) 490 491 return is_load_successful /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages/paddlehub/finetune/task.py in main_program(self) 331 def main_program(self): 332 if not self.env.is_inititalized: --> 333 self._build_env() 334 return self.env.main_program 335 /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages/paddlehub/finetune/task.py in _build_env(self) 244 with fluid.unique_name.guard(self.env.UNG): 245 self.config.strategy.execute( --> 246 self.loss, self._base_data_reader, self.config) 247 248 if self.is_train_phase: /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages/paddlehub/finetune/strategy.py in execute(self, loss, data_reader, config) 132 scheduled_lr = adam_weight_decay_optimization( 133 loss, warmup_steps, max_train_steps, self.learning_rate, --> 134 main_program, self.weight_decay, self.lr_scheduler) 135 136 return scheduled_lr /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages/paddlehub/finetune/optimization.py in adam_weight_decay_optimization(loss, warmup_steps, num_train_steps, learning_rate, main_program, weight_decay, scheduler) 77 param_list[param.name].stop_gradient = True 78 ---> 79 _, param_grads = optimizer.minimize(loss) 80 81 if weight_decay > 0: </opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages/decorator.py:decorator-gen-144> in minimize(self, loss, startup_program, parameter_list, no_grad_set, grad_clip) /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages/paddle/fluid/wrapped_decorator.py in impl(func, args, kwargs) 23 def impl(func, args, *kwargs): 24 wrapped_func = decorator_func(func) ---> 25 return wrapped_func(args, *kwargs) 26 27 return impl /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages/paddle/fluid/dygraph/base.py in impl(args, **kwargs) 85 def impl(args, kwargs): 86 with switch_tracer_mode_guard(is_train=False): ---> 87 return func(args, kwargs) 88 89 return impl /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages/paddle/fluid/optimizer.py in minimize(self, loss, startup_program, parameter_list, no_grad_set, grad_clip) 592 startup_program=startup_program, 593 parameter_list=parameter_list, --> 594 no_grad_set=no_grad_set) 595 596 if grad_clip is not None and framework.in_dygraph_mode(): /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages/paddle/fluid/optimizer.py in backward(self, loss, startup_program, parameter_list, no_grad_set, callbacks) 491 with program_guard(program, startup_program): 492 params_grads = append_backward(loss, parameter_list, --> 493 no_grad_set, callbacks) 494 # Note: since we can't use all_reduce_op now, 495 # dgc_op should be the last op of one grad. /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages/paddle/fluid/backward.py in append_backward(loss, parameter_list, no_grad_set, callbacks) 569 grad_to_var, 570 callbacks, --> 571 input_grad_names_set=input_grad_names_set) 572 573 # Because calc_gradient may be called multiple times, /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages/paddle/fluid/backward.py in append_backward_ops(block, ops, target_block, no_grad_dict, grad_to_var, callbacks, input_grad_names_set) 308 # Getting op's corresponding grad_op 309 grad_op_desc, op_grad_to_var = core.get_grad_op_desc( --> 310 op.desc, cpt.to_text(no_grad_dict[block.idx]), grad_sub_block_list) 311 312 # If input_grad_names_set is not None, extend grad_op_descs only when EnforceNotMet: Input ShapeTensor cannot be found in Op reshape2 at [/paddle/paddle/fluid/framework/op_desc.cc:306] PaddlePaddle Call Stacks: 0 0x7f1e9ac86750p void paddle::platform::EnforceNotMet::Init<char const>(char const, char const, int) + 352 1 0x7f1e9ac86ac9p paddle::platform::EnforceNotMet::EnforceNotMet(std::__exception_ptr::exception_ptr, char const, int) + 137 2 0x7f1e9adf7d7fp paddle::framework::OpDesc::Input(std::string const&) const + 207 3 0x7f1e9b2c762cp paddle::framework::details::OpInfoFiller<paddle::operators::Reshape2GradMaker, (paddle::framework::details::OpInfoFillType)2>::operator()(char const, paddle::framework::OpInfo) const::{lambda(paddle::framework::OpDesc const&, std::unordered_set<std::string, std::hashstd::string, std::equal_tostd::string, std::allocatorstd::string > const&, std::unordered_map<std::string, std::string, std::hashstd::string, std::equal_tostd::string, std::allocator<std::pair<std::string const, std::string> > >, std::vector<paddle::framework::BlockDesc, std::allocatorpaddle::framework::BlockDesc* > const&)#1 (closed)}::operator()(paddle::framework::OpDesc const&, std::unordered_set<std::string, std::hashstd::string, std::equal_tostd::string, std::allocatorstd::string > 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paddle::framework::details::OpInfoFiller<paddle::operators::Reshape2GradMaker, (paddle::framework::details::OpInfoFillType)2>::operator()(char const, paddle::framework::OpInfo) const::{lambda(paddle::framework::OpDesc const&, std::unordered_set<std::string, std::hashstd::string, std::equal_tostd::string, std::allocatorstd::string > const&, std::unordered_map<std::string, std::string, std::hashstd::string, std::equal_tostd::string, std::allocator<std::pair<std::string const, std::string> > >, std::vector<paddle::framework::BlockDesc, std::allocatorpaddle::framework::BlockDesc* > const&)#1 (closed)}>::_M_invoke(std::_Any_data const&, paddle::framework::OpDesc const&, std::unordered_set<std::string, std::hashstd::string, std::equal_tostd::string, std::allocatorstd::string > const&, std::unordered_map<std::string, std::string, std::hashstd::string, std::equal_tostd::string, std::allocator<std::pair<std::string const, std::string> > >, std::vector<paddle::framework::BlockDesc, 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